"""simple docstring"""


import argparse
from collections import OrderedDict
from pathlib import Path

import torch

from transformers import (
    VisualBertConfig,
    VisualBertForMultipleChoice,
    VisualBertForPreTraining,
    VisualBertForQuestionAnswering,
    VisualBertForVisualReasoning,
)
from transformers.utils import logging


logging.set_verbosity_info()
a				=logging.get_logger(__name__)

a				=[
    ('bert.bert', 'visual_bert'),
    ('bert.cls', 'cls'),
    ('bert.classifier', 'cls'),
    ('token_type_embeddings_visual', 'visual_token_type_embeddings'),
    ('position_embeddings_visual', 'visual_position_embeddings'),
    ('projection', 'visual_projection'),
]

a				=[
    'nlvr2_coco_pre_trained.th',
    'nlvr2_fine_tuned.th',
    'nlvr2_pre_trained.th',
    'vcr_coco_pre_train.th',
    'vcr_fine_tune.th',
    'vcr_pre_train.th',
    'vqa_coco_pre_trained.th',
    'vqa_fine_tuned.th',
    'vqa_pre_trained.th',
]





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   Optional[Any]:




						'''simple docstring'''



						lowerCamelCase__   =torch.load(a__  ,       map_location="cpu"							)
						return sd





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase=rename_keys_prefix							)				->   Optional[int]:




						'''simple docstring'''



						lowerCamelCase__   =OrderedDict()
						lowerCamelCase__   =torch.arange(config.max_position_embeddings							).expand((1, -1)							)
						# detector_d = OrderedDict()
						for key in d:
												if "detector" in key:
																		# detector_d[key.replace('detector.','')] = d[key]
																		continue
												lowerCamelCase__   =key
												for name_pair in rename_keys_prefix:
																		lowerCamelCase__   =new_key.replace(name_pair[0]  ,       name_pair[1]							)
												lowerCamelCase__   =d[key]
												if key == "bert.cls.predictions.decoder.weight":
																		# Old bert code didn't have `decoder.bias`, but was added separately
																		lowerCamelCase__   =new_d["cls.predictions.bias"]
						return new_d





@torch.no_grad()
def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   List[Any]:




						'''simple docstring'''



						assert (
						    checkpoint_path.split("/"							)[-1] in ACCEPTABLE_CHECKPOINTS
						), F'''The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'''

						# Get Config
						if "pre" in checkpoint_path:
												lowerCamelCase__   ="pretraining"
												if "vcr" in checkpoint_path:
																		lowerCamelCase__   ={"visual_embedding_dim": 512}
												elif "vqa_advanced" in checkpoint_path:
																		lowerCamelCase__   ={"visual_embedding_dim": 2048}
												elif "vqa" in checkpoint_path:
																		lowerCamelCase__   ={"visual_embedding_dim": 2048}
												elif "nlvr" in checkpoint_path:
																		lowerCamelCase__   ={"visual_embedding_dim": 1024}
												else:
																		raise NotImplementedError(F'''No implementation found for `{checkpoint_path}`.'''							)
						else:
												if "vcr" in checkpoint_path:
																		lowerCamelCase__   ={"visual_embedding_dim": 512}
																		lowerCamelCase__   ="multichoice"
												elif "vqa_advanced" in checkpoint_path:
																		lowerCamelCase__   ={"visual_embedding_dim": 2048}
																		lowerCamelCase__   ="vqa_advanced"
												elif "vqa" in checkpoint_path:
																		lowerCamelCase__   ={"visual_embedding_dim": 2048, "num_labels": 3129}
																		lowerCamelCase__   ="vqa"
												elif "nlvr" in checkpoint_path:
																		lowerCamelCase__   ={
																		    "visual_embedding_dim": 1024,
																		    "num_labels": 2,
																		}
																		lowerCamelCase__   ="nlvr"

						lowerCamelCase__   =VisualBertConfig(**a__							)

						# Load State Dict
						lowerCamelCase__   =load_state_dict(a__							)

						lowerCamelCase__   =get_new_dict(a__  ,       a__							)

						if model_type == "pretraining":
												lowerCamelCase__   =VisualBertForPreTraining(a__							)
						elif model_type == "vqa":
												lowerCamelCase__   =VisualBertForQuestionAnswering(a__							)
						elif model_type == "nlvr":
												lowerCamelCase__   =VisualBertForVisualReasoning(a__							)
						elif model_type == "multichoice":
												lowerCamelCase__   =VisualBertForMultipleChoice(a__							)

						model.load_state_dict(a__							)
						# Save Checkpoints
						Path(a__							).mkdir(exist_ok=a__							)
						model.save_pretrained(a__							)


if __name__ == "__main__":
	a				=argparse.ArgumentParser()
	# Required parameters
	parser.add_argument('orig_checkpoint_path', type=str, help='A path to .th on local filesystem.')
	parser.add_argument('pytorch_dump_folder_path', type=str, help='Path to the output PyTorch model.')
	a				=parser.parse_args()
	convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase__  =					{
    '''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''', '''DebertaOnnxConfig'''],
    '''tokenization_deberta''': ['''DebertaTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					['''DebertaTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''DebertaForMaskedLM''',
       '''DebertaForQuestionAnswering''',
       '''DebertaForSequenceClassification''',
       '''DebertaForTokenClassification''',
       '''DebertaModel''',
       '''DebertaPreTrainedModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFDebertaForMaskedLM''',
       '''TFDebertaForQuestionAnswering''',
       '''TFDebertaForSequenceClassification''',
       '''TFDebertaForTokenClassification''',
       '''TFDebertaModel''',
       '''TFDebertaPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
   from .tokenization_deberta import DebertaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_deberta_fast import DebertaTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_deberta import (
          DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          DebertaForMaskedLM,
          DebertaForQuestionAnswering,
          DebertaForSequenceClassification,
          DebertaForTokenClassification,
          DebertaModel,
          DebertaPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_deberta import (
          TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFDebertaForMaskedLM,
          TFDebertaForQuestionAnswering,
          TFDebertaForSequenceClassification,
          TFDebertaForTokenClassification,
          TFDebertaModel,
          TFDebertaPreTrainedModel,
      )


else:
   import sys

   lowerCamelCase__  =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




